Fault diagnosis for the motor drive system of urban transit based on improved Hidden Markov Model

Huang, Darong; Ke, Lanyan; Chu, Xiaoyan; Zhao, Ling; Mi, Bo

doi:10.1016/j.microrel.2018.01.017

Cited by 44 publications

(22 citation statements)

References 12 publications

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“…For instance, Shen and Jiang [2] examined the basic components and key technical parameters of the current monitoring system for URT trains. Focusing on the URT trains, Darong et al [3] differentiated between the front and rear tractions of the electric drive system, through analysis on rectification, inversion, modulation and demodulation. Li et al [4] divided the current monitoring system of the URT into several subsystems, and determined the basic elements of that system.…”

Section: Literature Reviewmentioning

confidence: 99%

Design and Simulation of an Intelligent Current Monitoring System for Urban Rail Transit

Yao

Zhao

et al. 2020

IEEE Access

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Many urban rail transit (URT) systems adopt the DC traction power supply system. Because of the impedance and incomplete ground insulation of the running track, it is inevitable for a part of the traction current to flow into the ground from the track, creating the stray current. This type of current causes great safety hazards to the metal structures in and near the URT system. Considering the power supply mode of the URT, this paper explores the different resistances in each power supply section under unilateral power supply and bilateral power supply. Then, the defects of the current discharge method were identified in the context of stray current protection. To solve these defects, the backpropagation neural network (BPNN) was adopted to build a discharge flow prediction model. On this basis, an intelligent current monitoring system was established for the URT. Finally, the authors simulated the impact of each factor on stray current, and verified the reliability and stability of the proposed monitoring system. Compared with predicted values and the actual values, the prediction agrees with the actual data very well. INDEX TERMS Urban rail transit (URT), backpropagation neural network (BPNN), stray current, current monitoring system.

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Section: Literature Reviewmentioning

confidence: 99%

Design and Simulation of an Intelligent Current Monitoring System for Urban Rail Transit

Yao

Zhao

et al. 2020

IEEE Access

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“…Sun et al 56 introduced rough set and IFS, based on which an intuitionistic uncertaintyrough set was presented and the reduction algorithm was improved to enhance the accuracy and rapidity of fault diagnosis. Darong et al 57 analyzed hidden Markov model (HMM) algorithm and improved the algorithm for fault diagnosis of motor equipment of urban rail transit. The authors had designed an online fault classification system with an adaptive model and achieved a good rate recognition in motor equipment.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis based on TOPSIS method with Manhattan distance

Jiang

Wang

Deng

et al. 2019

Advances in Mechanical Engineering

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Fault diagnosis is important for the maintenance of machinery equipment. Due to the randomness and fuzziness of fault, the relationship between fault types and their characteristics are complicated. Therefore, the determination of fault type is a challenging part of machinery fault diagnosis with the traditional method. To tackle this problem, a fault diagnosis approach based on the technique for order performance by similarity to ideal solution with Manhattan distance is presented in this article. First, the similarity measure between the fault model and the detection sample is constructed based on the Manhattan distance. Then, the similarity is transformed into intuitionistic fuzzy set and the generated intuitionistic fuzzy set is fused by the intuitionistic fuzzy weighted averaging operator. On this basis, the technique for order performance by similarity to the ideal solution approach is utilized to obtain the final rank to ascertain the fault type. The proposed method can handle an intricate relationship between multiple fault types and their various fault characteristics and better express uncertain information. Finally, a fault diagnosis example of the machine rotor and comparative study are conducted to illustrate the application and the effectiveness of the proposed method.

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“…However, the above methods are operated in the special imaging situation to achieve the insulator defect detection, it has great limitation to the natural captured insulator images in damage detection. With the developing of highspeed digital imaging and processing technique, intelligent identification technology began to be used in high-voltage safe operation and long-distance power transmission lines inspection [11][12][13][14][15][16]. Figure 1 is the illustration of electrical safe operation and transmission inspection, the electrical insulator (glass/ceramic material) image is captured by UAV (unmanned aerial vehicle), which is suitable for several kinds electrical safe inspection.…”

Section: Introductionmentioning

confidence: 99%

An Electrical Insulator Defects Detection Method Combined Human Receptive Field Model

Guo

Yao

et al. 2018

Journal of Control Science and Engineering

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Nondestructive inspection of electrical insulators subjected to the high electrical stress and environmental damage is fundamental for reliable operation of a transmission lines. The breakage and defect of the insulator have great influence on the safe of transmission lines, and insulator defect detection with difference types is a complex work. This paper proposed an insulator defect detection method inspired by human receptive field model, which meets the requirements for detecting defect insulator in a simple background. In this method, the defect detection combined human receptive field model of human visual system is constructed and applied on the different insulators, so as to achieve accurate detection of the insulator defected parts. Experimental results show that the method can accurately and robustly detect the defect (such as cracks and damage) of electrical insulator in case of noise affect.

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Fault diagnosis for the motor drive system of urban transit based on improved Hidden Markov Model

Cited by 44 publications

References 12 publications

Design and Simulation of an Intelligent Current Monitoring System for Urban Rail Transit

Design and Simulation of an Intelligent Current Monitoring System for Urban Rail Transit

Fault diagnosis based on TOPSIS method with Manhattan distance

An Electrical Insulator Defects Detection Method Combined Human Receptive Field Model

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